A Survey of Commonsense Knowledge Organization, Structuring and Categorization

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Abstract

Commonsense knowledge (CK) in artificial intelligence (AI), is an expanding field of research. Because CK is intrinsically implicit, current datadriven machine learning models are still far from competent compared to humans in commonsense reasoning tasks. To minimize the gap between machine learning models with the goal of artificial general intelligence (AGI), researchers propose to collect such CK from human and text corpus for commonsense augmentation. Over the years there have been many ways that commonsense knowledge (in AI) has been implemented. However, there has not been a systematic review conducted on how CK can be organized, structured and categorized. The aim of this paper is to do a literature survey on how existing knowledge sources organize, structure and categorize within the general frameworks of CK. The organization can decide the design schema of a knowledge graph (KG), the structuring decides the format and the categorization decides what dimensions and criteria a KG employs.